#coding = utf-8
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

#随机生成1000个点，围绕在y=0.1x+0.3的直线周围
num_points = 1000
vectors_set = []
for i in range(num_points):
    x1 = np.random.normal(0.0, 0.55)
    y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0, 0.03)
    vectors_set.append([x1, y1])

#生成一些样本
x_data = [v[0] for v in vectors_set]
y_data = [v[1] for v in vectors_set]

plt.scatter(x_data, y_data, c='r')
plt.show()

#生成1维的W矩阵,取值是[-1,1]之间的随机数
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='W')
#生成1维的矩阵，初始值是0
b = tf.Variable(tf.zeros([1], name='b'))
#经过计算得出预估值
y = W * x_data + b
#以预估值y和实际值y_data之间的均方误差作为损失
loss = tf.reduce_mean(tf.square(y-y_data), name='loss')
#采用梯度下降法来优化参数
optimizer = tf.train.GradientDescentOptimizer(0.5)
#训练的过程就是最小化这个误差
train = optimizer.minimize(loss, name='train')
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
#初始化的W和b值是多少
print("W =", sess.run(W), "b =", sess.run(b), "loss =", sess.run(loss))
#执行20次训练
for step in range(20):
    sess.run(train)
    #输出训练好的W和b
    print("W =", sess.run(W), "b =", sess.run(b), "loss =", sess.run(loss))